Intro to ggplotly()

library(plotly)

nfl_qbr_plot <- nfl_qbr %>% 
  ggplot(aes(x = total_epa, y = qbr_total, label = short_name)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_label() +
  theme_minimal() +
  labs(
    x = "EPA", y = "QBR", 
    title = "EPA is correlated with QBR"
    )

nfl_qbr_plot

ggplotly(nfl_qbr_plot)

Intro to plot_ly()

• Any plot made with plot_ly() uses the JavaScript library plotly.js

• plot_ly() interfaced directly with plotly.js

• plot_ly has arguments that fit into the “Grammar of Graphics”:

  • x, y
  • color, stroke, span, symbol, linetype

There is a family of add_* functions: - add_lines() - add_bars - add_histogram2d() - add_contour - add_boxplot - …and many more!

The plotly package takes a pure functional approach to a layered grammar of graphics, meaning (almost) every function anticipates a plotly object as input to it’s first argument and returns a modified version of that plotly object.

Example: the layout() function anticipates a plotly object in it’s first argument and it’s other arguments add and/or modify various layout components of that object (e.g., the title):

layout(
  plot_ly(nfl_qbr %>% filter(game_week == 3), x = ~short_name, y = ~total_epa, color = ~rank),
  title = "QB Total EPA for Week 3"
)

Notice that data manipulation verbs from the dplyr package (such as filter()) can be used to transform the data underlying a plotly object!

Or, in a more cleaner fashion:

nfl_qbr %>% 
  filter(game_week == 3) %>% 
  plot_ly(x = ~short_name, y = ~total_epa, color = ~rank) %>% 
  add_bars() 

Add a layer of text using the summarized counts. Note that the global x mapping, as well as the other mappings local to this text layer (text and y), reflect data values from step 3

nfl_qbr %>% 
  filter(game_week == 3) %>% 
  plot_ly(x = ~short_name, y = ~total_epa, color = ~rank) %>% 
  add_bars() %>% 
  add_text(
    text = ~team,
    textposition = "top middle",
    cliponaxis = F
  )

To recap:

1. Globally assign short_name,total_epa, rank to x, y and color, respectively

2. Add a bars layer (which inherits the y from plot_ly)

3. Use dplyr verbs to modify the data underlying the plotly object

4. Add additional layers (like a layer of text)

5. Share

Saving plotly/ggplotly objects

Plotly/ggplotly objects are also htmlwidgets so any methods used for saving htmlwidgets also works for plotly/ggplotly objects: - Saving and embedding HTML: htmlwidgets::saveWidget() function (also works for leaflet, DT, etc.) - Exporting static images: orca() as .svg or .pdf

Combining multiple views

p1 <- plot_ly(nfl_qbr, x = ~total_epa, y = ~qbr_total) %>% 
  add_lines(name = "qbr total")

p2 <- plot_ly(nfl_qbr, x = ~total_epa, y = ~qb_plays) %>% 
  add_lines(name = "qb plays")

subplot(p1, p2, shareX = T)

gg <- nfl_qbr %>%
  filter(team %in% c("ARI", "ATL", "CHI", "CAR", "DAL")) %>% 
  ggplot(aes(x = total_epa, y = qbr_total, color = team)) +
  geom_point(aes(frame = game_week)) 

ggplotly(gg)

Arranging multiple views

Since plotly objects are also htmlwidgets, any method that works for arranging htmlwidgets also works for plotly objects.

Common ways to arrange components in a single web-page:

1. flexdashboard: An R package for arranging components into an opinionated dashboard layout. This package is essentially a special rmarkdown template that uses a simple markup syntax to define the layout.

2. Bootstrap’s grid layout: Both the crosstalk and shiny packages provide ways to arrange numerous components via Bootstrap’s (a popular HTML/CSS framework) grid layout system.

3. CSS flexbox: If you know some HTML and CSS, you can leverage CSS flexbox to arrange components via the htmltools package.

knitr::include_graphics("/Users/atoumi/Desktop/Graphics/figs/sharing.png")

We’ll see how crosstalk, flexdashboard and DT work together! For shiny, check out this talk by Carson Sievert: https://talks.cpsievert.me/20191115/#1